The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropri...

Abstract This paper proposes a method which using density index function to sparse LS-SVM in highdimensional feature space, and gives a new method which takes each sample point as a clustering center to make hypersphere, so as to determine the fuzzy membership function in high-dimensional feature space, thus to establish a new fuzzy least squares support vector machine model, So it is different...

A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an ur...

We proposed the support vector machine (SVM)-based equalisation schemes for direct-sequence ultra wideband (UWB) systems. The severe intersymbol interference caused by the UWB channel was formulated as a pattern classification problem in the SVM-based equaliser, which operates in two main modes: training and detection. We also applied the least squares support vector classifiers (LS-SVCs) to re...

This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one fo...

In this paper, we propose a new feature selection approach for the recently proposed Least Squares Projection Twin Support Vector Machine (LSPTSVM) for binary classification. 1-norm is used in our feature selection objective so that only non-zero elements in weight vectors will be chosen as selected features. Also, the Tikhonov regularization term is incorporated to the objective of our approac...

A scheme of direct adaptive H∞ control based on least squares support vector machines (LS-SVM) is proposed for a class of nonlinear uncertain systems. In this method, LS-SVM is employed to construct the adaptive controller, and an on-line learning rule for the weighting vector and bias is derived. A parameter selection method based on the genetic algorithm (GA) is given for LS-SVM regression wi...

Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, ther...

In this paper, we formulate a least squares version of the one-class support vector fuzzy machine (LS one-class SVFM) which is combined with the fuzzy set theory. The parameters in the proposed algorithm, such as weight vector and bias term, are fuzzy numbers. Our model only needs to solve a system of linear equations, instead of a complex quadratic programming problem (QPP) solved in one-class...

This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the n...